R ile analize başlarken¹

Derleyen Serdar Balcı, MD, Pathologist

2019-09-24

knitr::opts_chunk$set(
    message = FALSE,
    warning = FALSE,
    comment = NA,
    include = FALSE,
    tidy = TRUE
)

R nerede kullanılır

Veri düzenleme
İstatistik analiz
Web sayfası hazırlama (Statik/Dinamik)
Sunum hazırlama
Programlama
Otomatik, periodik ve tekrarlanabilir rapor hazırlama
pdf, html, ppt oluşturma
tez yazma
kitap yazma
CV oluşturma
poster hazırlama
rapor şablonu oluşturma
…

R generation

R yıllar içinde çok fazla değişim gösterdi

https://rss.onlinelibrary.wiley.com/doi/10.1111/j.1740-9713.2018.01169.x

:scale 30%

R yükleme

http://www.youtube.com/watch?v=XcBLEVknqvY

R-project

https://cran.r-project.org/

RStudio

https://www.rstudio.com/

https://www.rstudio.com/products/rstudio/download/

https://moderndive.com/2-getting-started.html

RStudio eklentileri

Discover and install useful RStudio addins

https://cran.r-project.org/web/packages/addinslist/README.html

https://rstudio.github.io/rstudioaddins/

devtools::install_github("rstudio/addinexamples", type = "source")

MacOS için

X11

https://www.xquartz.org/

Java OS

https://support.apple.com/kb/dl1572

R zor şeyler için kolay, kolay şeyler için zor

R makes easy things hard, and hard things easy
Aynı şeyi çok fazla şekilde yapmak mümkün

R Syntax Comparison::CHEAT SHEET

https://www.amelia.mn/Syntax-cheatsheet.pdf

#RStats — There are always several ways to do the same thing… nice example on with the identity matrix by @TeaStats https://t.co/O3GXdPiM32
— Colin Fay 🤘 (@_ColinFay) April 1, 2019

R paketleri

Neden paketler var

I love the #rstats community.
Someone is like, “oh hey peeps, I saw a big need for this mundane but difficult task that I infrequently do, so I created a package that will literally scrape the last bits of peanut butter out of the jar for you. It's called pbplyr.”
What a tribe.
— Frank Elavsky ᴰᵃᵗᵃ ᵂᶦᶻᵃʳᵈ (@Frankly_Data) July 3, 2018

Paketleri nereden bulabiliriz

Available CRAN Packages By Name
https://cran.r-project.org/web/packages/available_packages_by_name.html
CRAN Task Views
https://cran.r-project.org/web/views/
Bioconductor
https://www.bioconductor.org
RecommendR
http://recommendr.info/
pkgsearch
CRAN package search
https://github.com/metacran/pkgsearch
CRANsearcher
https://github.com/RhoInc/CRANsearcher
Awesome R
https://awesome-r.com/

Kendi paket evrenini oluştur

pkgverse: Build a Meta-Package Universe
https://cran.r-project.org/web/packages/pkgverse/index.html

R paket yükleme

install.packages("tidyverse", dependencies = TRUE)
install.packages("jmv", dependencies = TRUE)
install.packages("questionr", dependencies = TRUE)
install.packages("Rcmdr", dependencies = TRUE)
install.packages("summarytools")

Paket çağırma

# require(tidyverse) require(jmv) require(questionr) library(summarytools)
# library(gganimate)

R için yardım bulma

Vignette

:scale 80%

RDocumentation https://www.rdocumentation.org
R Package Documentation https://rdrr.io/
GitHub
Stackoverflow

https://stackoverflow.com/

Google uygun anahtar kelime

How I use #rstats
h/t @ThePracticalDev pic.twitter.com/erRnTG0Ujr
— Emily Bovee (@ebovee09) August 10, 2018

Google’da ararken [R] yazmak da işe yarayabiliyor.

searcher package 📦

Awesome Cheatsheet https://github.com/detailyang/awesome-cheatsheet

http://cran.r-project.org/doc/contrib/Baggott-refcard-v2.pdf

https://www.rstudio.com/resources/cheatsheets/

Awesome R

https://github.com/qinwf/awesome-R#readme

https://awesome-r.com/

Twitter

https://twitter.com/hashtag/rstats?src=hash

Use Reproducible Examples When Asking

Got a question to ask on @SlackHQ or post on @github? No time to read the long post on how to use reprex? Here is a 20-second gif for you to format your R codes nicely and for others to reproduce your problem. (An example from a talk given by @JennyBryan) #rstat pic.twitter.com/gpuGXpFIsX
— ZhiYang (@zhiiiyang) October 18, 2018

Keeping up to date with R news
https://masalmon.eu/2019/01/25/uptodate/

R studio ile proje oluşturma

https://support.rstudio.com/hc/en-us/articles/200526207-Using-Projects

RStudio ile veri yükleme

https://support.rstudio.com/hc/en-us/articles/218611977-Importing-Data-with-RStudio

Excel

SPSS

CSV

Veriyi görüntüleme

Spreadsheet users using #rstats: where's the data?#rstats users using spreadsheets: where's the code?
— Leonard Kiefer (@lenkiefer) July 7, 2018

Veriyi görüntüleme

View(data)

data

head

tail

glimpse

str

skimr::skim()

Veriyi değiştirme

Veriyi kod ile değiştirelim

Veriyi eklentilerle değiştirme

:scale 50%

RStudio aracılığıyla recode

questionr paketi kullanılacak

:scale 50%

https://juba.github.io/questionr/articles/recoding_addins.html

Basit tanımlayıcı istatistikler

summary()

mean

median

min

max

sd

table()

library(readr)
irisdata <- read_csv("data/iris.csv")

jmv::descriptives(data = irisdata, vars = "Sepal.Length", splitBy = "Species", 
    freq = TRUE, hist = TRUE, dens = TRUE, bar = TRUE, box = TRUE, violin = TRUE, 
    dot = TRUE, mode = TRUE, sum = TRUE, sd = TRUE, variance = TRUE, range = TRUE, 
    se = TRUE, skew = TRUE, kurt = TRUE, quart = TRUE, pcEqGr = TRUE)


 DESCRIPTIVES

 Descriptives                                          
 ───────────────────────────────────────────────────── 
                          Species       Sepal.Length   
 ───────────────────────────────────────────────────── 
   N                      setosa                  50   
                          versicolor              50   
                          virginica               50   
   Missing                setosa                   0   
                          versicolor               0   
                          virginica                0   
   Mean                   setosa                5.01   
                          versicolor            5.94   
                          virginica             6.59   
   Std. error mean        setosa              0.0498   
                          versicolor          0.0730   
                          virginica           0.0899   
   Median                 setosa                5.00   
                          versicolor            5.90   
                          virginica             6.50   
   Mode                   setosa                5.00   
                          versicolor            5.50   
                          virginica             6.30   
   Sum                    setosa                 250   
                          versicolor             297   
                          virginica              329   
   Standard deviation     setosa               0.352   
                          versicolor           0.516   
                          virginica            0.636   
   Variance               setosa               0.124   
                          versicolor           0.266   
                          virginica            0.404   
   Range                  setosa                1.50   
                          versicolor            2.10   
                          virginica             3.00   
   Minimum                setosa                4.30   
                          versicolor            4.90   
                          virginica             4.90   
   Maximum                setosa                5.80   
                          versicolor            7.00   
                          virginica             7.90   
   Skewness               setosa               0.120   
                          versicolor           0.105   
                          virginica            0.118   
   Std. error skewness    setosa               0.337   
                          versicolor           0.337   
                          virginica            0.337   
   Kurtosis               setosa              -0.253   
                          versicolor          -0.533   
                          virginica           0.0329   
   Std. error kurtosis    setosa               0.662   
                          versicolor           0.662   
                          virginica            0.662   
   25th percentile        setosa                4.80   
                          versicolor            5.60   
                          virginica             6.23   
   50th percentile        setosa                5.00   
                          versicolor            5.90   
                          virginica             6.50   
   75th percentile        setosa                5.20   
                          versicolor            6.30   
                          virginica             6.90   
 ─────────────────────────────────────────────────────

# install.packages('scatr')

scatr::scat(data = irisdata, x = "Sepal.Length", y = "Sepal.Width", group = "Species", 
    marg = "dens", line = "linear", se = TRUE)

summarytools

https://cran.r-project.org/web/packages/summarytools/vignettes/Introduction.html

library(summarytools)
summarytools::freq(iris$Species, style = "rmarkdown")

Frequencies

iris$Species

Type: Factor

	Freq	% Valid	% Valid Cum.	% Total	% Total Cum.
setosa	50	33.33	33.33	33.33	33.33
versicolor	50	33.33	66.67	33.33	66.67
virginica	50	33.33	100.00	33.33	100.00
<NA>	0			0.00	100.00
Total	150	100.00	100.00	100.00	100.00

summarytools::freq(iris$Species, report.nas = FALSE, style = "rmarkdown", headings = FALSE)

	Freq	%	% Cum.
setosa	50	33.33	33.33
versicolor	50	33.33	66.67
virginica	50	33.33	100.00
Total	150	100.00	100.00

with(tobacco, print(ctable(smoker, diseased), method = "render"))

Cross-Tabulation, Row Proportions

smoker * diseased

Data Frame: tobacco

	diseased
smoker	Yes				No				Total
Yes	125	(	41.9%	)	173	(	58.1%	)	298	(	100.0%	)
No	99	(	14.1%	)	603	(	85.9%	)	702	(	100.0%	)
Total	224	(	22.4%	)	776	(	77.6%	)	1000	(	100.0%	)

Generated by summarytools 0.9.4 (R version 3.6.0)
2019-09-24

with(tobacco, print(ctable(smoker, diseased, prop = "n", totals = FALSE), omit.headings = TRUE, 
    method = "render"))

Cross-Tabulation

smoker * diseased

Data Frame: tobacco

	diseased
smoker	Yes	No
Yes	125	173
No	99	603

Generated by summarytools 0.9.4 (R version 3.6.0)
2019-09-24

summarytools::descr(iris, style = "rmarkdown")

Descriptive Statistics

iris

N: 150

	Petal.Length	Petal.Width	Sepal.Length	Sepal.Width
Mean	3.76	1.20	5.84	3.06
Std.Dev	1.77	0.76	0.83	0.44
Min	1.00	0.10	4.30	2.00
Q1	1.60	0.30	5.10	2.80
Median	4.35	1.30	5.80	3.00
Q3	5.10	1.80	6.40	3.30
Max	6.90	2.50	7.90	4.40
MAD	1.85	1.04	1.04	0.44
IQR	3.50	1.50	1.30	0.50
CV	0.47	0.64	0.14	0.14
Skewness	-0.27	-0.10	0.31	0.31
SE.Skewness	0.20	0.20	0.20	0.20
Kurtosis	-1.42	-1.36	-0.61	0.14
N.Valid	150.00	150.00	150.00	150.00
Pct.Valid	100.00	100.00	100.00	100.00

descr(iris, stats = c("mean", "sd", "min", "med", "max"), transpose = TRUE, 
    headings = FALSE, style = "rmarkdown")

	Mean	Std.Dev	Min	Median	Max
Petal.Length	3.76	1.77	1.00	4.35	6.90
Petal.Width	1.20	0.76	0.10	1.30	2.50
Sepal.Length	5.84	0.83	4.30	5.80	7.90
Sepal.Width	3.06	0.44	2.00	3.00	4.40

# view(dfSummary(iris))

dfSummary(tobacco, plain.ascii = FALSE, style = "grid")

Data Frame Summary

tobacco

Dimensions: 1000 x 9
Duplicates: 2

No	Variable	Stats / Values	Freqs (% of Valid)	Graph	Valid	Missing
1	gender [factor]	1. F 2. M	489 (50.0%) 489 (50.0%)	IIIIIIIIII IIIIIIIIII	978 (97.8%)	22 (2.2%)
2	age [numeric]	Mean (sd) : 49.6 (18.3) min < med < max: 18 < 50 < 80 IQR (CV) : 32 (0.4)	63 distinct values	. . . . . : : : : : : . : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : :	975 (97.5%)	25 (2.5%)
3	age.gr [factor]	1. 18-34 2. 35-50 3. 51-70 4. 71 +	258 (26.5%) 241 (24.7%) 317 (32.5%) 159 (16.3%)	IIIII IIII IIIIII III	975 (97.5%)	25 (2.5%)
4	BMI [numeric]	Mean (sd) : 25.7 (4.5) min < med < max: 8.8 < 25.6 < 39.4 IQR (CV) : 5.7 (0.2)	974 distinct values	: : : : : : : : : : : : . : : : : : .	974 (97.4%)	26 (2.6%)
5	smoker [factor]	1. Yes 2. No	298 (29.8%) 702 (70.2%)	IIIII IIIIIIIIIIIIII	1000 (100%)	0 (0%)
6	cigs.per.day [numeric]	Mean (sd) : 6.8 (11.9) min < med < max: 0 < 0 < 40 IQR (CV) : 11 (1.8)	37 distinct values	: : : : : . . . . . .	965 (96.5%)	35 (3.5%)
7	diseased [factor]	1. Yes 2. No	224 (22.4%) 776 (77.6%)	IIII IIIIIIIIIIIIIII	1000 (100%)	0 (0%)
8	disease [character]	1. Hypertension 2. Cancer 3. Cholesterol 4. Heart 5. Pulmonary 6. Musculoskeletal 7. Diabetes 8. Hearing 9. Digestive 10. Hypotension [ 3 others ]	36 (16.2%) 34 (15.3%) 21 ( 9.5%) 20 ( 9.0%) 20 ( 9.0%) 19 ( 8.6%) 14 ( 6.3%) 14 ( 6.3%) 12 ( 5.4%) 11 ( 5.0%) 21 ( 9.5%)	III III I I I I I I I I	222 (22.2%)	778 (77.8%)
9	samp.wgts [numeric]	Mean (sd) : 1 (0.1) min < med < max: 0.9 < 1 < 1.1 IQR (CV) : 0.2 (0.1)	0.86!: 267 (26.7%) 1.04!: 249 (24.9%) 1.05!: 324 (32.4%) 1.06!: 160 (16.0%) ! rounded	IIIII IIII IIIIII III	1000 (100%)	0 (0%)

# First save the results

iris_stats_by_species <- by(data = iris, INDICES = iris$Species, FUN = descr, 
    stats = c("mean", "sd", "min", "med", "max"), transpose = TRUE)

# Then use view(), like so:

view(iris_stats_by_species, method = "pander", style = "rmarkdown")

Descriptive Statistics

iris

Group: Species = setosa
N: 50

	Mean	Std.Dev	Min	Median	Max
Petal.Length	1.46	0.17	1.00	1.50	1.90
Petal.Width	0.25	0.11	0.10	0.20	0.60
Sepal.Length	5.01	0.35	4.30	5.00	5.80
Sepal.Width	3.43	0.38	2.30	3.40	4.40

Group: Species = versicolor
N: 50

	Mean	Std.Dev	Min	Median	Max
Petal.Length	4.26	0.47	3.00	4.35	5.10
Petal.Width	1.33	0.20	1.00	1.30	1.80
Sepal.Length	5.94	0.52	4.90	5.90	7.00
Sepal.Width	2.77	0.31	2.00	2.80	3.40

Group: Species = virginica
N: 50

	Mean	Std.Dev	Min	Median	Max
Petal.Length	5.55	0.55	4.50	5.55	6.90
Petal.Width	2.03	0.27	1.40	2.00	2.50
Sepal.Length	6.59	0.64	4.90	6.50	7.90
Sepal.Width	2.97	0.32	2.20	3.00	3.80

# view(iris_stats_by_species)

data(tobacco)  # tobacco is an example dataframe included in the package
BMI_by_age <- with(tobacco, by(BMI, age.gr, descr, stats = c("mean", "sd", "min", 
    "med", "max")))
view(BMI_by_age, "pander", style = "rmarkdown")

Descriptive Statistics

BMI by age.gr

Data Frame: tobacco
N: 258

	18-34	35-50	51-70	71 +
Mean	23.84	25.11	26.91	27.45
Std.Dev	4.23	4.34	4.26	4.37
Min	8.83	10.35	9.01	16.36
Median	24.04	25.11	26.77	27.52
Max	34.84	39.44	39.21	38.37

BMI_by_age <- with(tobacco, by(BMI, age.gr, descr, transpose = TRUE, stats = c("mean", 
    "sd", "min", "med", "max")))

view(BMI_by_age, "pander", style = "rmarkdown", omit.headings = TRUE)

Descriptive Statistics

BMI by age.gr

Data Frame: tobacco
N: 258

	Mean	Std.Dev	Min	Median	Max
18-34	23.84	4.23	8.83	24.04	34.84
35-50	25.11	4.34	10.35	25.11	39.44
51-70	26.91	4.26	9.01	26.77	39.21
71 +	27.45	4.37	16.36	27.52	38.37

tobacco_subset <- tobacco[, c("gender", "age.gr", "smoker")]
freq_tables <- lapply(tobacco_subset, freq)

# view(freq_tables, footnote = NA, file = 'freq-tables.html')

what.is(iris)

$properties property value 1 class data.frame 2 typeof list 3 mode list 4 storage.mode list 5 dim 150 x 5 6 length 5 7 is.object TRUE 8 object.type S3 9 object.size 7256 Bytes

$attributes.lengths names class row.names 5 1 150

$extensive.is [1] “is.data.frame” “is.list” “is.object” “is.recursive” [5] “is.unsorted”

skimr

library(skimr)
skim(df)

DataExplorer

library(DataExplorer)
DataExplorer::create_report(df)

inspectdf

https://github.com/alastairrushworth/inspectdf

Grafikler

descr(tobacco, style = 'rmarkdown')

print(descr(tobacco), method = 'render', table.classes = 'st-small')

dfSummary(tobacco, style = 'grid', plain.ascii = FALSE)

print(dfSummary(tobacco, graph.magnif = 0.75), method = 'render')

Here, building up a #ggplot2 as slowly as possible, #rstats. Incremental adjustments. #rstatsteachingideas pic.twitter.com/nUulQl8bPh
— Gina Reynolds (@EvaMaeRey) August 13, 2018

Dreaming of a fancy #Rstats #ggplot #dataviz but still scared of typing #code? @_pvictorr esquisse package has you covered https://t.co/1vIDXcVAAF pic.twitter.com/RlTkptnrNv
— Radoslaw Panczak (@RPanczak) October 2, 2018

Bazı arayüzler

Rcmdr

library(Rcmdr)

Rcmdr::Commander()

A Comparative Review of the R Commander GUI for R

http://r4stats.com/articles/software-reviews/r-commander/

jamovi

https://www.jamovi.org/

https://blog.jamovi.org/2018/07/30/rj.html

R nereden öğrenilir

https://sbalci.github.io/MyRCodesForDataAnalysis/WhereToLearnR.nb.html

Sonraki Konular

RStudio ile GitHub kullanımı
R Markdown ve R Notebook ile tekrarlanabilir rapor
Hipotez testleri

Geri Bildirim

Geri bildirim için tıklayınız: Geri bildirim formu

# Save Final Data

saved data after analysis to `Data-After-Analysis.xlsx`.

saveRDS(mydata, "Data-After-Analysis.rds")

writexl::write_xlsx(mydata, "Data-After-Analysis.xlsx")

file.info("Data-After-Analysis.xlsx")$ctime

Libraries Used

citation("tidyverse")
citation("foreign")
citation("tidylog")
citation("janitor")
citation("jmv")
citation("tangram")
citation("finalfit")
citation("summarytools")
citation("ggstatplot")
citation("readxl")

Notes

Completed on 2019-09-24 19:34:31.

Serdar Balci, MD, Pathologist
drserdarbalci@gmail.com
https://rpubs.com/sbalci/CV
https://sbalci.github.io/
https://github.com/sbalci

Bu bir derlemedir, mümkün mertebe alıntılara referans vermeye çalıştım.↩︎

R ile analize başlarken1

Derleyen Serdar Balcı, MD, Pathologist

2019-09-24

R nerede kullanılır

R generation

R yükleme

R-project

RStudio

RStudio

RStudio eklentileri

MacOS için

X11

Java OS

R zor şeyler için kolay, kolay şeyler için zor

R paketleri

Neden paketler var

Paketleri nereden bulabiliriz

Kendi paket evrenini oluştur

R paket yükleme

Paket çağırma

R için yardım bulma

R studio ile proje oluşturma

RStudio ile veri yükleme

Excel

SPSS

CSV

Veriyi görüntüleme

Veriyi görüntüleme

Veriyi değiştirme

Veriyi kod ile değiştirelim

Veriyi eklentilerle değiştirme

RStudio aracılığıyla recode

Basit tanımlayıcı istatistikler

summarytools

Frequencies

iris$Species

Cross-Tabulation, Row Proportions

smoker * diseased

Cross-Tabulation

smoker * diseased

Descriptive Statistics

iris

Data Frame Summary

tobacco

Descriptive Statistics

iris

Descriptive Statistics

BMI by age.gr

Descriptive Statistics

BMI by age.gr

skimr

DataExplorer

inspectdf

Grafikler

Bazı arayüzler

Rcmdr

jamovi

R nereden öğrenilir

Sonraki Konular

Geri Bildirim

Libraries Used

Notes

R ile analize başlarken¹